context safety score
A score of 48/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
malicious redirect
script/meta redirect patterns detected in page source
malicious redirect
The page contains only a JavaScript onload redirect to '/lander' with no visible content. The entire page body is empty and the sole purpose of the HTML is to silently forward visitors to another path, a common technique used in phishing and scam lander chains to obscure the true destination from scanners and to stage multi-step redirect flows. (location: page.html, line 1: window.onload=function(){window.location.href="/lander"})
hidden content
The page renders no visible text (page-text.txt is empty) despite being a fully loaded page. All functional content is hidden from static analysis and users see nothing before being redirected, indicating deliberate content concealment. (location: page-text.txt (empty), page.html line 1)
brand impersonation
The domain 'kashpay.cc' uses a .cc TLD with a payment-themed name ('kash' + 'pay'), a pattern commonly used to impersonate or evoke legitimate payment brands (e.g., CashPay, KashPay fintech services). WHOIS privacy is unknown/redacted and domain age is null, consistent with a newly registered lookalike domain. (location: metadata.json: domain=kashpay.cc, whois.domain_age_days=null, whois.privacy_redacted=null)
phishing
Combination of signals strongly indicates a phishing operation: .cc TLD with payment-brand name, null domain age (newly registered), WHOIS data unavailable/redacted, no visible page content, and an immediate JavaScript redirect to '/lander'. This pattern is a hallmark of phishing lander infrastructure designed to harvest credentials or payment data at the /lander destination. (location: metadata.json, page.html)
curl https://api.brin.sh/domain/kashpay.ccCommon questions teams ask before deciding whether to use this domain in agent workflows.
kashpay.cc currently scores 48/100 with a suspicious verdict and medium confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this domain.
Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.
brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.
Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.
brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.
No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.
Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.
Learn more in threat detection docs, how scoring works, and the API overview.
Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.